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"How to find answers using Google search" matching MCP servers:

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    MCP server that wraps the Brave Answers API, enabling synchronous Q&A and asynchronous deep research with job submission, status polling, and result retrieval.
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    MIT
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    An MCP server that provides Google Search functionality with automatic API key rotation and intelligent quota management, enabling natural language search queries with advanced filtering options.
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    MIT
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    An MCP server that enables AI models to perform Google Web searches using the Gemini API, complete with citations and grounding metadata for accurate information retrieval. It is compatible with Claude Desktop and other MCP clients for real-time web access.
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    Apache 2.0
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    Provides AI-powered search and documentation tools using Google Vertex AI or Gemini API with real-time web search grounding, enabling technical queries, code analysis, documentation retrieval, and architecture recommendations to overcome LLM knowledge gaps.
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    MIT
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    A Model Context Protocol server that enables LLMs to perform web searches using Google's Custom Search API through a standardized interface.
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    MIT
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    MCP server that enables live web search via Gemini's built-in grounding, returning synthesized answers with source citations through a single web_search tool.
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
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    Apache 2.0
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    Enables searching and retrieving documents from a local folder to ground LLM answers in your files.
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    MIT
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    A DeepSeek-powered MCP server that provides high-accuracy web search using the native DeepSeek V4-Flash search API, offering standard and deep research tools that return cited answers and cross-verified reports.
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    MIT
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    Enables semantic search over local markdown specs, RFCs, and docs using Ollama and LanceDB, allowing AI agents to find relevant information by meaning rather than exact keywords.
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    Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.
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    MIT
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    Enables AI-powered web searches using Google's Gemini 2.5 models with Google Search Grounding. Supports multiple Gemini models (Flash, Flash-Lite, Pro) with configurable thinking budget and web search capabilities.
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    MIT
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    MCP search and evidence tool for AI agents. Rewrites queries, zooms into source domains, and returns sourced answers with metrics.
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    MIT
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    Semantic, on-demand skill retrieval for Claude Code that saves tokens and improves skill discovery by replacing the native skill listing with vector embedding search.
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    MIT
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    Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.
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    MIT
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    MCP server for searching files on macOS by name or content using semantic and lexical search, providing file paths and line numbers for agents to read from.
    MIT